ACLNDA:在异构图中预测非编码 RNA 与疾病关联的非对称图对比学习框架。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae533
Laiyi Fu, ZhiYuan Yao, Yangyi Zhou, Qinke Peng, Hongqiang Lyu
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引用次数: 0

摘要

非编码 RNA(ncRNA),包括长非编码 RNA(lncRNA)和 microRNA(miRNA),在基因表达调控中起着至关重要的作用,在疾病关联和医学研究中意义重大。准确的 ncRNA-疾病关联预测对于了解疾病机制和开发治疗方法至关重要。现有方法通常只关注单一任务,如 lncRNA-疾病关联(LDA)、miRNA-疾病关联(MDA)或 lncRNA-miRNA 相互作用(LMI),而未能利用异构图特征。我们提出的 ACLNDA 是一种非对称图对比学习框架,用于分析异嗜性 ncRNA-疾病关联。它从原始的 lncRNA、miRNA 和疾病关联中构建层间邻接矩阵,并使用 Top-K 层内相似性边构建方法形成三层异质图。与传统方法不同的是,为了同时考虑节点属性特征(ncRNA/疾病)和节点偏好特征(关联),ACLNDA采用了一种非对称但简单的图对比学习框架,以最大化一跳邻域上下文和两跳相似性,提取ncRNA-疾病特征,而不依赖于图增强或同亲假设,从而在保持数据完整性的同时降低了计算成本。我们的框架能够应用于各种潜在的 LDA、MDA 和 LMI 关联预测。进一步的实验结果表明,与其他现有的最先进的基线方法相比,我们的框架具有更优越的性能,这表明它具有为疾病诊断和治疗目标识别提供见解的潜力。ACLNDA 的源代码和数据可在 https://github.com/AI4Bread/ACLNDA 公开获取。
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ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.

Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics. We propose ACLNDA, an asymmetric graph contrastive learning framework for analyzing heterophilic ncRNA-disease associations. It constructs inter-layer adjacency matrices from the original lncRNA, miRNA, and disease associations, and uses a Top-K intra-layer similarity edges construction approach to form a triple-layer heterogeneous graph. Unlike traditional works, to account for both node attribute features (ncRNA/disease) and node preference features (association), ACLNDA employs an asymmetric yet simple graph contrastive learning framework to maximize one-hop neighborhood context and two-hop similarity, extracting ncRNA-disease features without relying on graph augmentations or homophily assumptions, reducing computational cost while preserving data integrity. Our framework is capable of being applied to a universal range of potential LDA, MDA, and LMI association predictions. Further experimental results demonstrate superior performance to other existing state-of-the-art baseline methods, which shows its potential for providing insights into disease diagnosis and therapeutic target identification. The source code and data of ACLNDA is publicly available at https://github.com/AI4Bread/ACLNDA.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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